Sensor measurement value calibration using sensor calibration data and a performance model

ABSTRACT

Techniques disclosed herein relate to determining a calibrated measurement value indicative of a physiological condition of a patient using sensor calibration data and a performance model. In some embodiments, the techniques involve obtaining one or more electrical signals from a sensing element of a sensing arrangement, where the one or more electrical signals are influenced by a physiological condition in a body of a patient. The techniques also involve obtaining calibration data associated with the sensing element from a data storage element of the sensing arrangement, converting the one or more electrical signals into one or more calibrated measurement parameters using the calibration data, obtaining a performance model associated with the sensing element, obtaining personal data associated with the patient, and determining, using the performance model and based on the personal data and the one or more calibrated measurement parameters, a calibrated output value indicative of the physiological condition.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/569,417, filed Sep. 12, 2019, entitled “MANUFACTURING CONTROLS FORSENSOR CALIBRATION USING FABRICATION MEASUREMENTS,” which is hereinincorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

Techniques disclosed herein relate generally to determining a calibratedmeasurement value indicative of a physiological condition of a patientusing sensor calibration data and a performance model.

BACKGROUND

Infusion pump devices and systems are relatively well known in themedical arts, for use in delivering or dispensing an agent, such asinsulin or another prescribed medication, to a patient. Control schemeshave been developed to allow insulin infusion pumps to monitor andregulate a patient's blood glucose level in a substantially continuousand autonomous manner. Rather than continuously sampling and monitoringa user's blood glucose level, which may compromise battery life,intermittently sensed glucose data samples are often utilized forpurposes of continuous glucose monitoring (CGM) or determining operatingcommands for the infusion pump.

Many continuous glucose monitoring (CGM) sensors measure the glucose inthe interstitial fluid (ISF). Typically, to achieve the desired level ofaccuracy and reliability and reduce the impact of noise and otherspurious signals, the sensor data is calibrated using a known good bloodglucose value, often obtained via a so-called “fingerstick measurement”using a blood glucose meters that measures the blood glucose in thecapillaries. However, performing such calibration measurements increasesthe patient burden and perceived complexity, and can be inconvenient,uncomfortable, or otherwise disfavored by patients. Moreover, ISFglucose measurements lag behind the blood glucose measurements based onthe time it takes glucose to diffuse from the capillary to theinterstitial space where it is measured by the CGM sensor, whichrequires signal processing (e.g., filtering) or other techniques tocompensate for physiological lag. Additionally, various factors can leadto transient changes in the sensor output, which may influence theaccuracy of the calibration. Degradation of sensor performance over timeor manufacturing variations may further compound these problems.Accordingly, it is desirable to provide sensor calibration in a mannerthat decreases the patient burden and improves the overall userexperience without compromising accuracy or reliability.

BRIEF SUMMARY

Techniques disclosed herein relate generally to determining a calibratedmeasurement value indicative of a physiological condition of a patientusing sensor calibration data and a performance model. The techniquesmay be practiced using a system comprising one or more processors andone or more processor-readable media; a processor-implemented method;and/or one or more non-transitory processor-readable media.

According to certain embodiments, the techniques may involve obtainingone or more electrical signals from a sensing element of a sensingarrangement, where the one or more electrical signals are influenced bya physiological condition in a body of a patient. The techniques mayalso involve obtaining calibration data associated with the sensingelement from a data storage element of the sensing arrangement,converting the one or more electrical signals into one or morecalibrated measurement parameters using the calibration data, obtaininga performance model associated with the sensing element, obtainingpersonal data associated with the patient, and determining a calibratedoutput value indicative of the physiological condition using theperformance model and based on the personal data and the one or morecalibrated measurement parameters.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures, which may beillustrated for simplicity and clarity and are not necessarily drawn toscale.

FIG. 1 depicts an exemplary embodiment of an infusion system;

FIG. 2 is a block diagram of an exemplary embodiment of a sensingarrangement suitable for use in the infusion system of FIG. 1 ;

FIG. 3 depicts a fabrication system for fabricating and calibrating asensing element suitable for use in the sensing arrangement of FIG. 2 ;

FIG. 4 is a cross-section of an electrode of an interstitial glucosesensing element suitable for fabrication by the fabrication system ofFIG. 3 for use in the sensing arrangement of FIG. 2 ;

FIG. 5 is a flow diagram of an exemplary fabrication model developmentprocess suitable for use with the fabrication system of FIG. 3 in one ormore exemplary embodiments;

FIG. 6 is a flow diagram of an exemplary sensor initialization processsuitable for use with the sensing arrangement of FIG. 2 in conjunctionwith the fabrication model development process of FIG. 5 in one or moreexemplary embodiments;

FIG. 7 is a flow diagram of an exemplary performance model developmentprocess suitable for use with a sensing arrangement in one or moreexemplary embodiments;

FIG. 8 is a flow diagram of an exemplary measurement process suitablefor use with a sensing arrangement in conjunction with the sensorinitialization process of FIG. 6 and the performance model developmentprocess of FIG. 7 in one or more exemplary embodiments;

FIG. 9 is a block diagram of a data management system suitable for usewith a sensing arrangement in connection with one or more of theprocesses of FIGS. 5-8 ; and

FIG. 10 is a flow diagram of an exemplary sensor initialization processsuitable for use with the sensing arrangement of FIG. 2 in conjunctionwith one or more of the processes of FIGS. 5-8 in one or more exemplaryembodiments.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description.

Exemplary embodiments of the subject matter described herein generallyrelate to calibrating sensing elements and related sensing arrangementsand devices that provide an output that is indicative of and/orinfluenced by one or more characteristics or conditions that are sensed,measured, detected, or otherwise quantified by the sensing element.While the subject matter described herein is not necessarily limited toany particular type of sensing application, exemplary embodiments aredescribed herein primarily in the context of a sensing element thatgenerates or otherwise provides electrical signals indicative of and/orinfluenced by a physiological condition in a body of a human user orpatient, such as, for example, interstitial glucose sensing elements.

As described in greater detail below, fabrication process measurementdata associated with an instance of a sensing element is utilized todetermine calibration data for converting electrical signals output bythat instance of the sensing element into one or more calibratedmeasurement parameters based on a calibration model associated with thesensing element. In this regard, the calibration model maps one or morefabrication process measurements corresponding to the area or region ofthe substrate where a particular instance of the sensing element wasmanufactured to calibration factors for determining one or morecalibration measurement parameters for the current instance of thesensing element. In exemplary embodiments, the calibration data isdetermined and stored or otherwise maintained in association with theinstance of the sensing element after fabrication but prior todeployment of the sensing element. Thereafter, during operation, thecalibration data may be utilized to convert electrical signals output bythat instance of the sensing element into one or more calibratedmeasurement parameters. In exemplary embodiments, a performance modelassociated with the sensing element is utilized to convert thecalibrated measurement parameters into a calibrated output valueindicative of the sensed physiological condition of the patient usingpersonal data associated with the patient or other data characterizingthe nature or manner of operation of the sensing element. In thismanner, calibrated measurement values for the physiological condition ofthe patient may be obtained without requiring a so-called “fingerstickmeasurement” or other reference measurements.

For purposes of explanation, exemplary embodiments of the subject matterare described herein as being implemented in conjunction with medicaldevices, such as portable electronic medical devices. Although manydifferent applications are possible, the following description may focuson embodiments that incorporate a fluid infusion device (or infusionpump) as part of an infusion system deployment. That said, the subjectmatter may be implemented in an equivalent manner in the context ofother medical devices, such as continuous glucose monitoring (CGM)devices, injection pens (e.g., smart injection pens), and the like. Forthe sake of brevity, conventional techniques related to infusion systemoperation, insulin pump and/or infusion set operation, and otherfunctional aspects of the systems (and the individual operatingcomponents of the systems) may not be described in detail here. Thatsaid, the subject matter described herein can be utilized more generallyin the context of overall diabetes management or other physiologicalconditions independent of or without the use of an infusion device orother medical device (e.g., when oral medication is utilized), and thesubject matter described herein is not limited to any particular type ofmedication. In this regard, the subject matter is not limited to medicalapplications and could be implemented in any device or application thatincludes or incorporates a sensing element.

Infusion System Overview

FIG. 1 depicts an exemplary embodiment of an infusion system 100 thatincludes, without limitation, a fluid infusion device (or infusion pump)102, a sensing arrangement 104, a command control device (CCD) 106, anda computer 108. The components of an infusion system 100 may be realizedusing different platforms, designs, and configurations, and theembodiment shown in FIG. 1 is not exhaustive or limiting. In practice,the infusion device 102 and the sensing arrangement 104 are secured atdesired locations on the body of a user (or patient), as illustrated inFIG. 1 . In this regard, the locations at which the infusion device 102and the sensing arrangement 104 are secured to the body of the patientin FIG. 1 are provided only as a representative, non-limiting, example.The elements of the infusion system 100 may be similar to thosedescribed in U.S. Pat. No. 8,674,288, the subject matter of which ishereby incorporated by reference in its entirety.

In the illustrated embodiment of FIG. 1 , the infusion device 102 isdesigned as a portable medical device suitable for infusing a fluid, aliquid, a gel, or other medicament into the body of a user. In exemplaryembodiments, the infused fluid is insulin, although many other fluidsmay be administered through infusion such as, but not limited to, HIVdrugs, drugs to treat pulmonary hypertension, iron chelation drugs, painmedications, anti-cancer treatments, medications, vitamins, hormones, orthe like. In some embodiments, the fluid may include a nutritionalsupplement, a dye, a tracing medium, a saline medium, a hydrationmedium, or the like. Generally, the fluid infusion device 102 includes amotor or other actuation arrangement that is operable to linearlydisplace a plunger (or stopper) of a reservoir provided within the fluidinfusion device to deliver a dosage of fluid, such as insulin, to thebody of a patient. Dosage commands that govern operation of the motormay be generated in an automated manner in accordance with the deliverycontrol scheme associated with a particular operating mode, and thedosage commands may be generated in a manner that is influenced by acurrent (or most recent) measurement of the physiological condition inthe body of the patient. For example, in a closed-loop operating mode,dosage commands may be generated based on a difference between a current(or most recent) measurement of the interstitial fluid glucose level inthe body of the user and a target (or reference) glucose value. In thisregard, the rate of infusion may vary as the difference between acurrent measurement value and the target measurement value fluctuates.For purposes of explanation, the subject matter is described herein inthe context of the infused fluid being insulin for regulating a glucoselevel of a user (or patient); however, it should be appreciated thatmany other fluids may be administered through infusion, and the subjectmatter described herein is not necessarily limited to use with insulin.

The sensing arrangement 104 generally represents another medical devicethat includes the components of the infusion system 100 that areconfigured to sense, detect, measure or otherwise quantify aphysiological condition of the patient, and may include a sensor, amonitor, or the like, for providing data indicative of the conditionthat is sensed, detected, measured or otherwise monitored by the sensingarrangement. In this regard, the sensing arrangement 104 may includeelectronics and enzymes reactive to a biological condition, such as ablood glucose level, or the like, of the patient, and provide dataindicative of the blood glucose level to the infusion device 102, theCCD 106 and/or the computer 108. For example, the infusion device 102,the CCD 106 and/or the computer 108 may include a display for presentinginformation or data to the patient based on the sensor data receivedfrom the sensing arrangement 104, such as, for example, a currentglucose level of the patient, a graph or chart of the patient's glucoselevel versus time, device status indicators, alert messages, or thelike. In other embodiments, the infusion device 102, the CCD 106 and/orthe computer 108 may include electronics and software that areconfigured to analyze sensor data and operate the infusion device 102 todeliver fluid to the body of the patient based on the sensor data and/orpreprogrammed delivery routines. Thus, in exemplary embodiments, one ormore of the infusion device 102, the sensing arrangement 104, the CCD106, and/or the computer 108 includes a transmitter, a receiver, and/orother transceiver electronics that allow for communication with othercomponents of the infusion system 100, so that the sensing arrangement104 may transmit sensor data or monitor data to one or more of theinfusion device 102, the CCD 106 and/or the computer 108. While thesubject matter is described herein in the context of glucose sensing, itshould be appreciated the subject matter described herein is notnecessarily limited to glucose sensing and may implemented in anequivalent manner for any number of other different enzymaticsubstances, such as, for example, lactate, beta-hydroxybutyrate,creatinine, etc.

Still referring to FIG. 1 , in various embodiments, the sensingarrangement 104 may be secured to the body of the patient or embedded inthe body of the patient at a location that is remote from the locationat which the infusion device 102 is secured to the body of the patient.In various other embodiments, the sensing arrangement 104 may beincorporated within the infusion device 102. In other embodiments, thesensing arrangement 104 may be separate and apart from the infusiondevice 102, and may be, for example, part of the CCD 106. In suchembodiments, the sensing arrangement 104 may be configured to receive abiological sample, analyte, or the like, to measure a condition of thepatient.

In some embodiments, the CCD 106 and/or the computer 108 may includeelectronics and other components configured to perform processing,delivery routine storage, and to control the infusion device 102 in amanner that is influenced by sensor data measured by and/or receivedfrom the sensing arrangement 104. By including control functions in theCCD 106 and/or the computer 108, the infusion device 102 may be madewith more simplified electronics. However, in other embodiments, theinfusion device 102 may include all control functions, and may operatewithout the CCD 106 and/or the computer 108. In various embodiments, theCCD 106 may be a portable electronic device. In addition, in variousembodiments, the infusion device 102 and/or the sensing arrangement 104may be configured to transmit data to the CCD 106 and/or the computer108 for display or processing of the data by the CCD 106 and/or thecomputer 108.

In some embodiments, the CCD 106 and/or the computer 108 may provideinformation to the patient that facilitates the patient's subsequent useof the infusion device 102. For example, the CCD 106 may provideinformation to the patient to allow the patient to determine the rate ordose of medication to be administered into the patient's body. In otherembodiments, the CCD 106 may provide information to the infusion device102 to autonomously control the rate or dose of medication administeredinto the body of the patient. In some embodiments, the sensingarrangement 104 may be integrated into the CCD 106. Such embodiments mayallow the patient to monitor a condition by providing, for example, asample of his or her blood to the sensing arrangement 104 to assess hisor her condition. In some embodiments, the sensing arrangement 104 andthe CCD 106 may be used for determining glucose levels in the bloodand/or body fluids of the patient without the use of, or necessity of, awire or cable connection between the infusion device 102 and the sensingarrangement 104 and/or the CCD 106.

In some embodiments, the sensing arrangement 104 and/or the infusiondevice 102 are cooperatively configured to utilize a closed-loop systemfor delivering fluid to the patient. Examples of sensing devices and/orinfusion pumps utilizing closed-loop systems may be found at, but arenot limited to, the following U.S. Pat. Nos. 6,088,608, 6,119,028,6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402,153 or U.S. PatentApplication Publication No. 2014/0066889, all of which are incorporatedherein by reference in their entirety. In such embodiments, the sensingarrangement 104 is configured to sense or measure a condition of thepatient, such as, blood glucose level or the like. The infusion device102 is configured to deliver fluid in response to the condition sensedby the sensing arrangement 104. In turn, the sensing arrangement 104continues to sense or otherwise quantify a current condition of thepatient, thereby allowing the infusion device 102 to deliver fluidcontinuously in response to the condition currently (or most recently)sensed by the sensing arrangement 104 indefinitely. In some embodiments,the sensing arrangement 104 and/or the infusion device 102 may beconfigured to utilize the closed-loop system only for a portion of theday, for example only when the patient is asleep or awake.

FIG. 2 depicts an exemplary embodiment of a sensing arrangement 200suitable for use as the sensing arrangement 104 in the infusion systemof FIG. 1 in accordance with one or more embodiments. The illustratedsensing device 200 includes, without limitation, a control module 204, asensing element 202, an output interface 208, and a data storage element(or memory) 208. The control module 204 is coupled to the sensingelement 202, the output interface 208, and the memory 206, and thecontrol module 204 is suitably configured to support the operations,tasks, and/or processes described herein.

The sensing element 202 generally represents the component of thesensing device 200 that is configured to generate, produce, or otherwiseoutput one or more electrical signals indicative of a condition that issensed, measured, or otherwise quantified by the sensing device 200. Inthis regard, the physiological condition of a user influences acharacteristic of the electrical signal output by the sensing element202, such that the characteristic of the output signal corresponds to oris otherwise correlative to the physiological condition that the sensingelement 202 is sensitive to. The sensing element 202 may be realized asa glucose sensing element that generates an output electrical signalhaving a current (or voltage) associated therewith that is correlativeto the interstitial fluid glucose level that is sensed or otherwisemeasured in the body of the patient by the sensing arrangement 104, 200.

Still referring to FIG. 2 , the control module 204 generally representsthe hardware, circuitry, logic, firmware and/or other component(s) ofthe sensing device 200 that is coupled to the sensing element 202 toreceive the electrical signals output by the sensing element 202 andperform various additional tasks, operations, functions and/or processesdescribed herein. For example, the control module 204 may filter,analyze or otherwise process the electrical signals received from thesensing element 202 to obtain a measurement value for conversion into acalibrated measurement of the interstitial fluid glucose level.Additionally, in one or more embodiments, the control module 204 alsoimplements or otherwise executes a calibration application module thatcalculates or otherwise determines calibrated measurement parametersbased on the measurement value using calibration data associated withthe sensing element 202 that is stored or otherwise maintained in thememory 206, as described in greater detail below. The calibratedmeasurement parameters may then be utilized to obtain a calibratedmeasurement value for the patient's interstitial glucose level, asdescribed in greater detail below.

Depending on the embodiment, the control module 204 may be implementedor realized with a general purpose processor, a microprocessor, acontroller, a microcontroller, a state machine, a content addressablememory, an application specific integrated circuit, a field programmablegate array, any suitable programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof, designed to perform the functions described herein. In thisregard, the steps of a method or algorithm described in connection withthe embodiments disclosed herein may be embodied directly in hardware,in firmware, in a software module executed by the control module 204, orin any practical combination thereof. In exemplary embodiments, thecontrol module 204 includes or otherwise accesses the data storageelement or memory 206. The memory 206 may be realized using any sort ofRAM, ROM, flash memory, registers, hard disks, removable disks, magneticor optical mass storage, short or long term storage media, or any othernon-transitory computer-readable medium capable of storing programminginstructions, code, or other data for execution by the control module204. The computer-executable programming instructions, when read andexecuted by the control module 204, cause the control module 204 toperform the tasks, operations, functions, and processes described ingreater detail below.

In some embodiments, the control module 204 includes ananalog-to-digital converter (ADC) or another similar samplingarrangement that samples or otherwise converts the output electricalsignal received from the sensing element 202 into corresponding digitalmeasurement data value correlative to the interstitial fluid glucoselevel sensed by the sensing element 202. In other embodiments, thesensing element 202 may incorporate an ADC and output a digitalmeasurement value. In one or more embodiments, the current of theelectrical signal output by the sensing element 202 is influenced by theuser's interstitial fluid glucose level, and the digital measurementdata value is realized as a current measurement value provided by an ADCbased on an analog electrical output signal from the sensing element202.

The output interface 208 generally represents the hardware, circuitry,logic, firmware and/or other components of the sensing arrangement 200that are coupled to the control module 204 for outputting data and/orinformation from/to the sensing device 200, for example, to/from theinfusion device 102, the CCD 106 and/or the computer 108. In thisregard, in exemplary embodiments, the output interface 208 is realizedas a communications interface configured to support communicationsto/from the sensing device 200. In such embodiments, the communicationsinterface 208 may include or otherwise be coupled to one or moretransceiver modules capable of supporting wireless communicationsbetween the sensing device 200 and another electronic device (e.g., aninfusion device 102 or another electronic device 106, 108 in an infusionsystem 100). Alternatively, the communications interface 208 may berealized as a port that is adapted to receive or otherwise be coupled toa wireless adapter that includes one or more transceiver modules and/orother components that support the operations of the sensing device 200described herein. In other embodiments, the communications interface 208may be configured to support wired communications to/from the sensingdevice 200. In yet other embodiments, the output interface 208 mayinclude or otherwise be realized as an output user interface element,such as a display element (e.g., a light-emitting diode or the like), adisplay device (e.g., a liquid crystal display or the like), a speakeror another audio output device, a haptic feedback device, or the like,for providing notifications or other information to the user. In suchembodiments, the output user interface 208 may be integrated with thesensing arrangement 104, 200 (e.g., within a common housing) orimplemented separately.

It should be understood that FIG. 2 is a simplified representation of asensing device 200 for purposes of explanation and is not intended tolimit the subject matter described herein in any way. In this regard,although FIG. 2 depicts the various elements residing within the sensingdevice 200, one or more elements of the sensing device 200 may bedistinct or otherwise separate from the other elements of the sensingdevice 200. For example, the sensing element 202 may be separate and/orphysically distinct from the control module 204 and/or thecommunications interface 208. Furthermore, features and/or functionalityof described herein as implemented by the control module 204 mayalternatively be implemented at the infusion device 102 or anotherdevice 106, 108 within an infusion system 100.

Sensor Fabrication

FIG. 3 depicts an exemplary embodiment of a fabrication system 300 fordeveloping calibration models for sensing elements fabricated on asubstrate 302. In this regard, multiple instances of a sensing elementmay be fabricated on a substrate 302, which is subsequently diced intosmaller discrete portions (or dies) containing a respective instance ofthe sensing element. In exemplary embodiments, the different instancesof electrochemical sensing elements are concurrently fabricated on orwithin regions 304 of the substrate 302, alternatively referred toherein as sensing regions, while process control monitors (PCM) areconcurrently fabricated on or within other regions 306 of the substrate302 that are adjacent to or otherwise in the vicinity of the sensingregions 304. For example, in the illustrated embodiments, the sensingregions 304 are arranged in vertically-oriented columns on the substrate302 with the PCM regions 306 being realized as vertically-orientedcolumns interposed between neighboring sensing regions 304. In thisregard, the PCM regions 306 may include multiple PCMs running verticallythroughout the length of the PCM regions 306, while the sensing regions304 include multiple instances of sensing elements running verticallythroughout the length of the sensing regions 304.

After and/or during fabrication, the PCMs fabricated within the PCMregions 306 are analyzed using one or more process measurement systems310 to obtain fabrication process measurements for each PCM fabricatedon the substrate 302. In this regard, the process measurement system 310is capable of measuring the biological, chemical, electrical, and/orphysical characteristics of the respective PCMs. The fabrication processmeasurement data obtained for each PCM may include, for example, glucoseoxidase (GOx) thickness, GOx activity, glucose limiting membrane (GLM)thickness, working electrode (WE) platinum imaginary impedance, counterelectrode (CE) platinum imaginary impedance, and human serum albumin(HSA) concentration. That said, it should be noted that the subjectmatter described herein is not intended to be limited to any particulartype or number of fabrication process measurements, and the fabricationprocess measurements could include measurements of any number ofdifferent properties or characteristics (e.g., dielectriccharacteristics, permeability, diffusivity, etc.). Additionally, oralternatively, in some embodiments, the fabrication process measurementsmay be obtained by directly measuring characteristics of the sensingelements on the sensing regions 304.

FIG. 4 depicts a cross-section of a working electrode 400 suitable forfabrication on the substrate 302 within sensing regions 304 for use inan interstitial glucose sensing element. Additionally, in someembodiments, dummy versions of the working electrode 400 may befabricated within the PCM regions 306 for purposes of obtainingfabrication process measurements. The working electrode 400 includes asubstrate or base layer 402 (e.g., polyimide) and an overlying platedmetallic layer 404 (e.g., chromium and gold). An electroplated layer 406(e.g., platinum) is provided on the metallic layer 404 between portionsof an insulating layer 408 (e.g., polyimide). A glucose oxidase layer410 is formed overlying the layer 406 by depositing a glucose oxidasesolution (e.g., via slot coating), and a human serum albumin (HSA) layer412 is formed overlying the glucose oxidase layer 410. An adhesive layer414 is provided overlying the HSA layer 412 to affix a glucose limitingmembrane (GLM) layer 416 overlying the working electrode 400. Thecounter electrode of the interstitial glucose sensing element may besimilar or substantially identical to the working electrode 400 butlacking the glucose oxidase layer 410.

To obtain fabrication process measurements, in an exemplary embodiment,an imaginary impedance of the working electrode (and similarly, theimaginary impedance for the counter electrode) is measured after aplatinum electroplating process to form layer 406. The GOx solutionactivity measurements may be acquired during or after the GOx solutionpreparation process prior to deposition, while the GOx thickness (e.g.,the thickness of layer 410) is measured after the slot coating andselective patterning processes over the working electrode 400. The HSAconcentration may be measured during or after the solution preparationprocess prior to spray coating the HSA layer 412 on the substrate, andthe GLM thickness (e.g., the thickness of layer 416) may be measuredprior to applying the GLM layer 416. In one or more embodiments, thesemeasurements are performed with respect to sacrificial or monitorinstances of the working electrode 400 fabricated within PCM regions 306on the substrate 302. In some embodiments, additional fabricationprocess measurements such as surface roughness or other topographicmeasurements may be obtained for the working electrode 400 during orafter fabrication (e.g., via interferometry).

It should be appreciated that FIG. 4 depicts a simplified representationof one exemplary embodiment of the working electrode 400, and practicalembodiments may include any number of additional and/or alternativelayers (e.g., a high-density amine (HDA) layer, etc.). Accordingly, thesubject matter described herein is not intended to be limited to theembodiment depicted in FIG. 4 .

Referring again to FIG. 3 , the fabrication process measurementsobtained by the process measurement system 310 are provided to amodeling system 330. In one or more embodiments, the modeling system 330interpolates and/or extrapolates the fabrication process measurementdata for different PCMs to obtain representative fabrication processmeasurement data for a given instance of a sensing element fabricated onthe substrate 302. In this regard, the modeling system 330 may maintainan association between the location of a respective PCM on the substrate302 (e.g., a coordinate location) and the corresponding fabricationprocess measurements obtained for that respective PCM. Thereafter, basedon the location of a respective sensing element fabricated on thesubstrate 302, the modeling system 330 may identify the subset of PCMsthat are neighboring, adjacent, or otherwise proximate to thatrespective sensing element, obtain the fabrication process measurementdata for the identified subset of PCMs, and then average or otherwisecombine the fabrication process measurement data for the different PCMsof the subset based on the relationship between the location of therespective sensing element relative to the locations of the differentPCMs to obtain representative fabrication process measurement data forthe location on the substrate 302 corresponding to where the respectivesensing element was fabricated.

In exemplary embodiments, each of the different sensing elementsfabricated within the sensing regions 304 are analyzed using one or moretesting systems 320 to obtain reference measurement outputs for eachsensing element fabricated on the substrate 302 in response to one ormore known reference inputs. For example, in exemplary embodiments, thesensing elements fabricated within the sensing regions 304 are realizedas electrochemical interstitial glucose sensing elements that areexposed to known concentrations of glucose, with the testing system 320including glucose sensor transmitters, recorders, ammeters, voltmeters,or suitable measuring instruments capable of measuring characteristicsof the resulting output signals that are generated or otherwise providedby the glucose sensing elements. In this regard, the reference outputmeasurement parameters obtained for each sensing element may include oneor more of the electrical current output by the sensing element inresponse to a reference glucose concentration, electrochemical impedancespectroscopy (EIS) values (for one or more frequencies) or othermeasurements indicative of a characteristic impedance associated withthe sensing element in response to a reference glucose concentration,counter electrode voltage (Vctr) (e.g., the difference between counterelectrode potential and working electrode potential), and the like. Forexample, a glucose sensor transmitter may include potentiostat hardwareand firmware cooperatively configured to collect electrical currentmeasurements corresponding to the electrical current through the workingelectrode resulting from an applied bias potential and reaction of theglucose oxidase layer(s) of the working electrode of the sensing elementto a reference glucose concentration, while also calculating the counterelectrode voltage (Vctr) by difference of the measured counter electrodepotential and working electrode potential. The glucose sensortransmitter may also be configured to perform electrochemical impedancespectroscopy at various time intervals and at multiple frequencies withrespect to the electrical current and voltage at the working electrode.

The reference output measurement parameters obtained by the testingsystem 320 are provided to the modeling system 330, which maintains thereference output measurement parameters in association with therespective instance of a sensing element fabricated on the substrate302. In this regard, the modeling system 330 maintains an associationbetween the reference output measurement parameters for a respectivesensing element fabricated on the substrate 302 and the representativefabrication process measurements for that respective sensing elementfabricated on the substrate 302. As described in greater detail below,based on the relationships between the fabrication process measurementdata and the reference measurement output data for the various differentinstances of a sensing element, the modeling system 330 determinescalibration models for calculating or otherwise predicting outputmeasurement parameters for a sensing element as a function of one ormore fabrication process measurement parameters associated with thatsensing element. In this regard, the output measurement parametersdetermined using the calibration models are effectively calibrated toaccount for fabrication process variations, and accordingly, arealternatively referred to herein as calibrated measurement parameters.

Manufacturing Calibration

FIG. 5 depicts an exemplary embodiment of a fabrication modeldevelopment process 500 for developing calibration models that mapfabrication process measurements for a sensing element to correspondingcalibration measurement parameters for that sensing element. The varioustasks performed in connection with the fabrication model developmentprocess 500 may be performed by hardware, firmware, software executed byprocessing circuitry, or any combination thereof. For illustrativepurposes, the following description may refer to elements mentionedabove in connection with FIGS. 1-3 . It should be appreciated that thefabrication model development process 500 may include any number ofadditional or alternative tasks, the tasks need not be performed in theillustrated order and/or the tasks may be performed concurrently, and/orthe fabrication model development process 500 may be incorporated into amore comprehensive procedure or process having additional functionalitynot described in detail herein. Moreover, one or more of the tasks shownand described in the context of FIG. 5 could be omitted from a practicalembodiment of the fabrication model development process 500 as long asthe intended overall functionality remains intact.

The illustrated fabrication model development process 500 begins byreceiving or otherwise obtaining fabrication process measurements fromdifferent PCM regions on a substrate having sensing elements fabricatedthereon (task 502). For example, as described above, the processmeasurement system 310 analyzes the PCM regions 306 on the substrate 302to obtain, for each PCM region 306, one or more measurements of thephysical characteristics of the respective PCM region 306. Themeasurements of the physical characteristics of the PCM regions 306 areprovided to the modeling system 330 which maintains associations betweenthe respective measurements and the respective locations of therespective PCM regions 306 on the substrate 302. The fabrication modeldevelopment process 500 also receives or otherwise obtains measurementsignal outputs from the different sensing elements fabricated on thesubstrate (task 504). For example, as described above, the testingsystem 320 tests or otherwise analyzes the different sensing elements304 fabricated on the substrate 302 to obtain, for each sensing element304, one or more reference measurement outputs generated or otherwiseprovided by the respective sensing element 304 in response to one ormore known reference inputs. The reference measurement outputs areprovided to the modeling system 330 which maintains associations betweenthe respective reference measurement outputs and the respectivelocations of the respective sensing elements 304 on the substrate 302.

In the illustrated embodiment, the fabrication model development process500 continues by assigning fabrication process measurements to each ofthe sensing elements fabricated on the substrate and maintainingassociations between the assigned fabrication process measurements andreference measurement outputs for each sensing element (tasks 506, 508).For example, as described above, using the coordinate location for wherea respective sensing element 304 was fabricated on the substrate 302,the modeling system 330 may calculate or otherwise determine estimatedfabrication process measurements for that coordinate location based onfabrication process measurements from different PCM regions 306 aroundthat coordinate location. In this regard, interpolation techniques(e.g., multivariate interpolation) may be employed to derive an estimateof what the physical characteristics of the respective sensing element304 are likely to be based on fabrication process measurementsassociated with neighboring PCM regions 306 in a manner that accountsfor the spatial relationships between the coordinate location of thesensing element 304 relative to the respective coordinate locations ofthe neighboring PCM regions 306. For each respective sensing element304, the modeling system 330 may maintain an association between therepresentative or estimated fabrication process measurements assigned tothe respective sensing element 304, the reference measurement outputsobtained from that respective sensing element 304, and coordinatelocation on the substrate 302 where that respective sensing element 304was fabricated. Additionally, or alternatively, some embodiments mayobtain fabrication process measurements directly from the respectivesensing element 304, which, may be utilized individually or incombination with estimated fabrication process measurements derived fromthe PCM regions 306. Accordingly, the subject matter described herein isnot necessarily limited to any particular location from which thefabrication process measurements are obtained.

Still referring to FIG. 5 , the fabrication model development process500 utilizes the relationships between the reference measurement outputsand fabrication process measurements for different sensing elements tocalculate or otherwise determine a predictive model for determiningcalibrated measurement parameters as a function of the fabricationprocess measurements (task 510). In this regard, for each differentmeasurement parameter, the modeling system 330 may utilize machinelearning or artificial intelligence techniques to determine whichcombination of fabrication process measurement parameters are correlatedto or predictive of the respective calibration measurement parameter,and then determine a corresponding equation, function, or model forcalculating a calibration factor (or scaling factor) for determining aneffectively calibrated value of the parameter of interest based on thatset of input variables. Thus, the model is capable of characterizing ormapping a particular combination of one or more fabrication processmeasurement parameters to a calibration factor for determining aneffectively calibrated value for the calibration parameter of interest(e.g., electrical current output, EIS value, or the like).

For example, an interstitial sensing element may be designed to producea particular amount of current in response to the reference glucoseconcentration utilized by the testing system 320, alternatively referredto herein as the design current. For each sensing element, the modelingsystem 330 may divide the measured reference electrical current outputfor the respective sensing element in response to the reference glucoseconcentration by the design current to determine an output electricalcurrent calibration factor for each sensing element. Thereafter, themodeling system 330 may utilize machine learning to identify whichcombination of fabrication process measurement parameters are correlatedto or predictive of the output electrical current calibration factors,and then determine a corresponding equation, function, or model forcalculating the output electrical current calibration factor based onthat subset of fabrication process measurement input variables.Similarly, measured reference EIS values for the respective sensingelements may be divided by a design EIS value to determine EIScalibration factor for each sensing element, which, in turn, areutilized to determine a corresponding equation, function, or model forcalculating an EIS calibration factor based on a subset of correlativefabrication process measurement input variables.

As another example, a neural network model may be developed using linearregression and an appropriate activation function, which could varydepending on the calibration parameter of interest. The fabricationmeasurement inputs and calibration parameter outputs are structured intocorresponding matrices or vectors, which are then fed into a lossfunction with initial values for cost, weights, and bias for mapping theinput matrix to the output matrix. The initial values are then inputinto the linear equation and activation portions of the neuron toinitialize the neural network. The cost is then computed and a gradientdescent performed to determine updated weights and an updated bias as aresult of the gradient descent and an optimized characteristic learningrate. The process is then iteratively repeated for a desired number ofiterations (e.g., 1000 iterations) to “learn” the weights and bias to beutilized as part of the predictive model for the calibrationparameter(s).

It should be noted that the subset of fabrication process measurementparameters that are predictive of or correlative for a particularcalibration measurement parameter may vary from other calibrationmeasurement parameters. Additionally, the relative weightings applied tothe respective fabrication measurement parameters of that predictivesubset may also vary from other calibration measurement parameters whomay have common predictive subsets, based on differing correlationsbetween a particular fabrication measurement variable and the referencemeasurement data for that calibration parameter. In this regard, eachmeasurement has a specific weight depending on the degree of influence(or lack thereof) with respect to the particular measurement parameter.For example, the electrical current output may be most stronglycorrelated to the GLM thickness and GOx thickness. It should also benoted that any number of different machine learning techniques may beutilized to determine what fabrication process measurement parametersare predictive for a calibration measurement parameter of interest, suchas, for example, genetic programming, support vector machines, Bayesiannetworks, probabilistic machine learning models, or other Bayesiantechniques, fuzzy logic, heuristically derived combinations, or thelike. Additionally, in practice, prior to model development, thepreceding tasks 502, 504, 506, 508 of the fabrication model developmentprocess 500 may be performed multiple times for multiple differentsubstrates until a sufficient number of sensing elements andcorresponding data sets have been obtained to achieve the desired levelof accuracy or reliability for the resulting models.

FIG. 6 depicts an exemplary embodiment of a sensor initializationprocess 600 for utilizing calibration models from the fabrication modeldevelopment process 500 to configure sensing elements after fabricationand prior to deployment. The various tasks performed in connection withthe sensor initialization process 600 may be performed by hardware,firmware, software executed by processing circuitry, or any combinationthereof. For illustrative purposes, the following description may referto elements mentioned above in connection with FIGS. 1-3 . It should beappreciated that the sensor initialization process 600 may include anynumber of additional or alternative tasks, the tasks need not beperformed in the illustrated order and/or the tasks may be performedconcurrently, and/or the sensor initialization process 600 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 6 couldbe omitted from a practical embodiment of the sensor initializationprocess 600 as long as the intended overall functionality remainsintact.

In exemplary embodiments, the sensor initialization process 600 isperformed with respect to sensing elements fabricated after calibrationmodels have been developed to allow calibration factors or scalingfactors to be assigned to the sensing elements based on fabricationprocess measurement data without requiring testing to empiricallydetermine calibration data for a respective sensing element. Similar tothe fabrication model development process 500, the sensor initializationprocess 600 begins by receiving or otherwise obtaining fabricationprocess measurements from different PCM regions on a substrate havingsensing elements fabricated thereon, identifying the location of therespective sensing element on the substrate, and determiningrepresentative fabrication process measurement data for the respectivesensing element based on its location (tasks 602, 604, 606). Asdescribed above, the substrate 302 is provided to a process measurementsystem 310 for measuring physical characteristics of different PCMregions 306 on the substrate 302. Based on the coordinate location wherea respective sensing element was fabricated, estimated fabricationprocess measurements for the sensing element are calculated or otherwisedetermined based on the respective sensing element's spatialrelationship with respect to neighboring PCM regions 306, for example,by a multivariate interpolation of the fabrication process measurementsassociated with the neighboring PCM regions 306.

After obtaining the fabrication process measurement parameters for thecurrent instance of the sensing element of interest, the sensorinitialization process 600 continues by applying the calibration modelsdeveloped for that sensing element to the estimated fabrication processmeasurements to determine calibration factors or scaling factors for thecurrent instance of the sensing element (task 608). In this regard, foreach respective calibration measurement parameter, the correlativesubset of the estimated fabrication process measurements for thatrespective calibration measurement parameter are input or otherwiseprovided to the calibration model for that respective calibrationmeasurement parameter to calculate a corresponding calibration factorfor converting output from the sensing element into a calibrated valuefor that respective calibration measurement parameter. Thus, for aninterstitial glucose sensing element, a first calibration factor may bedetermined for converting the electrical current output from theinterstitial glucose sensing element into a calibrated electricalcurrent output, a second calibration factor may be determined forconverting an EIS value into a calibrated EIS value, and so on.

After determining the calibration factors for the different calibrationmeasurement parameters, the sensor initialization process 600 continuesby storing or otherwise maintaining the calibration data in associationwith the sensing element (task 610). In this regard, in exemplaryembodiments, for each respective calibration measurement parameter, acorresponding calibration factor is stored or otherwise maintained inthe memory 206 of the sensing arrangement 200 that includes therespective sensing element 202. Thereafter, when the sensing arrangement200 is in use, the control module 204 utilizes the stored calibrationfactors in the memory 206 to convert the different measured values forthe calibration measurement parameters determined based on the output ofthe sensing element 202 (e.g., the electrical current output, EISvalues, and the like) into calibrated values. For example, the controlmodule 204 may determine a raw or uncalibrated EIS value based on theoutput signals provided by the sensing element 202 and then multiply orotherwise convert the EIS value into a calibrated EIS value using themodel-derived EIS calibration factor stored in the memory 206. In thismanner, the sensing arrangement 200 may be configured to outputmeasurement parameter values that are effectively calibrated account forfabrication process variations without requiring testing of the sensingelement 202.

FIG. 7 depicts an exemplary embodiment of a performance modeldevelopment process 700 for developing one or more models that mapcalibration measurement parameters provided by a sensing device into acalibrated measurement value. The various tasks performed in connectionwith the performance model development process 700 may be performed byhardware, firmware, software executed by processing circuitry, or anycombination thereof. For illustrative purposes, the followingdescription may refer to elements mentioned above in connection withFIGS. 1-3 . It should be appreciated that the performance modeldevelopment process 700 may include any number of additional oralternative tasks, the tasks need not be performed in the illustratedorder and/or the tasks may be performed concurrently, and/or theperformance model development process 700 may be incorporated into amore comprehensive procedure or process having additional functionalitynot described in detail herein. Moreover, one or more of the tasks shownand described in the context of FIG. 7 could be omitted from a practicalembodiment of the performance model development process 700 as long asthe intended overall functionality remains intact.

In the illustrated embodiment, the performance model development process700 obtains patient data for a number of different patients, obtains oneor more sets of calibrated measurement parameters and correspondingreference measurement values for each patient, and maintains theassociation between an individual patient's patient data, calibratedmeasurement parameters, and reference measurement values (tasks 702,704, 706, 708). In exemplary embodiments, the patient data includes oneor more of the patient's age, gender, height, weight, body mass index(BMI), demographic data, and/or other parameters characterizing thepatient. For each patient, at least one set of calibrated measurementparameters (e.g., output electrical current measurement, EIS values, andthe like) is also obtained and maintained in association with acontemporaneous and/or corresponding reference measurement value for thephysiological condition of the patient. For example, for a fingerstickor other reference blood glucose measurement for the patient, thecontemporaneous or current calibrated measurement parameters output byan interstitial sensing arrangement 104, 200 may be stored or otherwisemaintained in association with the reference blood glucose measurementfor developing a model for calculating or otherwise predicting a bloodglucose measurement as a function of one or more of the calibratedmeasurement parameters.

The performance model development process 700 continues by calculatingor otherwise determining a model for calculating or otherwisedetermining a measurement value as a function of the patient data andone or more calibration measurement parameters (task 710). For example,machine learning may be utilized to determine which combination ofpatient data parameters and calibration measurement parameters arecorrelated to or predictive of the reference blood glucose measurementvalues, and then determine a corresponding equation, function, or modelfor calculating a blood glucose measurement value based on that set ofinput variables. Thus, the sensor performance model is capable ofcharacterizing or mapping a particular combination of patient data andcalibrated measurement parameters to a blood glucose measurement valuethat is effectively calibrated without requiring a fingerstick or otherreference measurement to calibrate instances of the sensing arrangement104, 200. Depending on the embodiment, the sensor performance model maybe stored at the sensing arrangement 104, 200 (e.g., in memory 206) orat another remote device or database.

In exemplary embodiments, the time (or timestamps) associated with thepatient data parameters and calibration measurement parameters may alsobe utilized as an input to the sensor performance model. For example,outputs from the sensing arrangement 104, 200 may be timestamped toallow for determination of the elapsed time since sensor insertion, timeof day, or other temporal variable, which, in turn may be utilized as aninput variable correlative to the sensor performance. In this manner,the sensor performance model may account for time-dependent signalchanges or variations that may be specific to a particular patient (orsubset of patients), fabrication process measurement(s) and/orcombinations thereof

FIG. 8 depicts an exemplary embodiment of a measurement process 800 fordetermining a calibrated measurement value for a physiological conditionof a patient using the calibration models developed using the process500 of FIG. 5 and the sensor performance model developed using theprocess 700 of FIG. 7 without requiring the patient to perform anyadditional calibration processes. The various tasks performed inconnection with the measurement process 800 may be performed byhardware, firmware, software executed by processing circuitry, or anycombination thereof. For illustrative purposes, the followingdescription may refer to elements mentioned above in connection withFIGS. 1-3 . It should be appreciated that the measurement process 800may include any number of additional or alternative tasks, the tasksneed not be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the measurement process 800 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 8 couldbe omitted from a practical embodiment of the measurement process 800 aslong as the intended overall functionality remains intact.

The illustrated measurement process 800 begins by receiving or otherwiseobtaining one or more measurement signals from a sensing element andutilizing calibration data associated with the sensing element toconvert the measurement signal(s) into calibrated measurement parameters(tasks 802, 804, 806). For example, the control module 204 may sample orotherwise obtain the measurement signal(s) output by the interstitialglucose sensing element 202 that are influenced by the interstitialglucose level of the patient, and based thereon, determine raw oruncalibrated values for the output electrical current through thesensing element 202, EIS values characterizing the impedance of thesensing element 202, and the like. Thereafter, the control module 204obtains the stored calibration factors associated with the sensingelement 202 from the memory 206 and utilizes the stored calibrationfactors to convert the uncalibrated values into a calibrated outputelectrical current, calibrated EIS values, and the like.

Additionally, the measurement process 800 receives or otherwise obtainspatient data associated with or otherwise characterizing the currentpatient and utilizes the sensor performance model to determine acalibrated measurement value using the current patient's data and thecalibrated measurement parameters (tasks 808, 810, 812). For example,the patient's age, gender, height, weight, body mass index (BMI),demographic data, and/or other parameters characterizing the patient maybe stored or otherwise maintained in the memory 206 of the sensingarrangement 104, 200 (or alternatively, at another device 102, 106, 108in an infusion system 100) along with the sensor performance modelassociated with the type or configuration of sensing element 202 and/orsensing arrangement 104, 200 currently being utilized. The currentvalues for the calibrated measurement parameters that have beenpreviously identified as input variables to the sensor performance modelthat are correlative to calibrated measurement values are input orotherwise provided to the sensor performance model along with the subsetof patient data that was previously identified as correlative tocalibrated measurement values. In this manner, the control module 204 atthe sensing arrangement 104, 200 (or alternatively, at another device102, 106, 108 in an infusion system 100) utilizes the equation orfunction provided by the sensor performance model and its associatedweightings of input variables to calculate or otherwise determine acalibrated sensor glucose measurement value based on one or more of thecalibrated output electrical current, calibrated EIS values, and thelike in conjunction with one or more patient data parameters. Theresulting calibrated sensor glucose measurement value may then beutilized to generate corresponding dosage commands for operating theinfusion device 102 or perform other actions pertaining to management ofthe patient's glucose levels. For example, a closed-loop operating modeutilized to control the infusion device 102 may calculate or otherwisedetermine a dosage command based on a difference between the calibratedsensor glucose measurement value and a target glucose value for thepatient and autonomously operate a motor or other actuation arrangementof the infusion device 902 to deliver the commanded dosage of insulin tothe patient.

FIG. 9 depicts an exemplary embodiment of a data management system 900suitable for implementing the subject matter described herein. The datamanagement system 900 that includes, without limitation, a computingdevice 902 coupled to a database 904 that is also communicativelycoupled to one or more electronic devices 906 over a communicationsnetwork 908, such as, for example, the Internet, a cellular network, awide area network (WAN), or the like. It should be appreciated that FIG.9 depicts a simplified representation of a patient data managementsystem 900 for purposes of explanation and is not intended to limit thesubject matter described herein in any way.

In exemplary embodiments, the electronic devices 906 include one or moremedical devices, such as, for example, an infusion device, a sensingdevice, a monitoring device, and/or the like. Additionally, theelectronic devices 906 may include any number of non-medical clientelectronic devices, such as, for example, a mobile phone, a smartphone,a tablet computer, a smart watch, or other similar mobile electronicdevice, or any sort of electronic device capable of communicating withthe computing device 902 via the network 908, such as a laptop ornotebook computer, a desktop computer, or the like. In this regard, theelectronic devices 906 may also include one or more components of aprocess measurement system 310, a testing system 320 and/or a modelingsystem 330 configured to support the subject matter described herein.One or more of the electronic devices 906 may include or be coupled to adisplay device, such as a monitor, screen, or another conventionalelectronic display, capable of graphically presenting data and/orinformation pertaining to the physiological condition of a patient.Additionally, one or more of the electronic devices 906 also includes oris otherwise associated with a user input device, such as a keyboard, amouse, a touchscreen, a microphone, or the like, capable of receivinginput data and/or other information from a user of the electronic device906.

In exemplary embodiments, one or more of the electronic devices 906transmits, uploads, or otherwise provides data or information to thecomputing device 902 for processing at the computing device 902 and/orstorage in the database 904. For example, when an electronic device 906is realized as a sensing device, monitoring device, or other device thatincludes sensing element is inserted into the body of a patient orotherwise worn by the patient to obtain measurement data indicative of aphysiological condition in the body of the patient, the electronicdevice 906 may periodically upload or otherwise transmit the measurementdata to the computing device 902. In other embodiments, a clientelectronic device 906 may be utilized by a patient to manually define,input or otherwise provide data or information characterizing thepatient and then transmit, upload, or otherwise provide such patientdata to the computing device 902. In yet other embodiments, when theelectronic device 906 is realized as a component of a processmeasurement system 310, a testing system 320 and/or a modeling system330, the electronic device 906 may upload fabrication processmeasurement data, testing data, and/or other modeling data to thecomputing device 902 for processing at the computing device 902 and/orstorage in the database 904 (e.g., modeling data 920). For example, insome embodiments, the computing device 902 may obtain the fabricationprocess measurement data and testing data from the process measurementsystem 310 and the testing system 320, respectively, and then utilizethe received data to develop measurement parameter calibration factormodels by or at the computing device 902 (e.g., the modeling system 330is implemented at the computing device 902). In yet other embodiments,the computing device 902 may instead receive measurement parametercalibration factor models from the modeling system 330 for storageand/or maintenance at the database 904 for subsequent deployment toelectronic devices 906.

The computing device 902 generally represents a server or other remotedevice configured to receive data or other information from theelectronic devices 906, store or otherwise manage data in the database904, and analyze or otherwise monitor data received from the electronicdevices 906 and/or stored in the database 904. In practice, thecomputing device 902 may reside at a location that is physicallydistinct and/or separate from the electronic devices 906, such as, forexample, at a facility that is owned and/or operated by or otherwiseaffiliated with a manufacturer of one or more medical devices utilizedin connection with the patient data management system 900. For purposesof explanation, but without limitation, the computing device 902 mayalternatively be referred to herein as a server, a remote server, orvariants thereof. The server 902 generally includes a processing systemand a data storage element (or memory) capable of storing programminginstructions for execution by the processing system, that, when read andexecuted, cause processing system to create, generate, or otherwisefacilitate the applications or software modules configured to perform orotherwise support the processes, tasks, operations, and/or functionsdescribed herein. Depending on the embodiment, the processing system maybe implemented using any suitable processing system and/or device, suchas, for example, one or more processors, central processing units(CPUs), controllers, microprocessors, microcontrollers, processing coresand/or other hardware computing resources configured to support theoperation of the processing system described herein. Similarly, the datastorage element or memory may be realized as a random access memory(RAM), read only memory (ROM), flash memory, magnetic or optical massstorage, or any other suitable non-transitory short or long term datastorage or other computer-readable media, and/or any suitablecombination thereof

In exemplary embodiments, the database 904 is utilized to store orotherwise maintain historical patient data 910 for a plurality ofdifferent patients. For example, as described above, the database 904may store or otherwise maintain reference blood glucose measurements(e.g., a fingerstick or metered blood glucose value) for differentpatients in association with the contemporaneous or current calibratedmeasurement parameters output by the respective sensing arrangement 104,200 associated with a respective patient at or around the time of therespective blood glucose measurement. Additionally, the patient data 910may maintain personal information associated with the differentpatients, including the respective patient's age, gender, height,weight, body mass index (BMI), demographic data, and/or other parameterscharacterizing the respective patient. In one or more embodiments, thedatabase 904 is also utilized to store or otherwise maintain modelingdata 920 that may be uploaded to and/or determined by the server 902,such as, for example, fabrication process measurement data, testingdata, calibration models, and/or the like.

In one or more embodiments, the server 902 utilizes the historicalpatient data 910 stored in the database 904 to determine a sensorperformance model for a particular type or configuration of sensingelement 202 and/or sensing arrangement 104, 200 in a similar manner asdescribed above in the context of FIG. 7 . Thereafter, the server 902may store or otherwise maintain the sensor performance model in thedatabase 904 and subsequently provides the sensor performance model toinstances of the particular type or configuration of sensing element 202and/or sensing arrangement 104, 200. For example, upon initialization ofa sensing arrangement 104, 200, 906, the control module 204 may beconfigured to download or otherwise obtain the appropriate sensorperformance model from the remote server 902 via the network 908.Thereafter, the control module 204 may utilize the sensor performancemodel in conjunction with the locally stored calibration factors inmemory 206 to determine calibrated glucose measurement values for thecurrent patient without requiring a fingerstick measurement or othercalibration procedure. In yet other embodiments, the sensor performancemodel may be provided to an infusion device 102, 906 or anotherelectronic device 106, 108, 906 in an infusion system 100 that isconfigured to receive calibrated measurement parameters from the sensingarrangement 104, 200. In such embodiments, the infusion device 102, 906or other electronic device 106, 108, 906 may utilize the obtained sensorperformance model to determine calibrated glucose measurement valuesusing calibrated measurement parameters provided by the sensingarrangement 104, 200 without requiring a fingerstick measurement orother calibration procedure.

By virtue of the subject matter described herein, individual sensingelements may be individually calibrated prior to deployment in a mannerthat accounts for fabrication process variations using measurement dataobtained from the substrate without requiring separate testing orcalibration steps after fabrication. Additionally, the calibratedmeasurement parameters may be utilized along with individual patientdata to determine calibrated measurement values for a physiologicalcondition of the patient without requiring the patient to performcalibration steps (e.g., obtaining fingerstick measurements, etc.).Incorporating time or other temporal variables into the sensorperformance model may also account or compensate for variability oraging of interstitial glucose sensing elements with respect to timeduring their respective lifespans.

Performance-based Manufacturing Controls for Manufacturing Calibration

FIG. 10 depicts an exemplary embodiment of a performance testing process1000 suitable for use in connection with the processes described abovein the context of FIG. 5-8 . In this regard, the performance testingprocess 1000 mitigates process variation by effectively filtering orotherwise excluding instances of a sensing element that represent cornercases (or process corners) and exhibit deviation in their respectiveoutput measurement signals relative to the probable distribution ofoutput measurement signals for that sensing element. The various tasksperformed in connection with the testing process 1000 may be performedby hardware, firmware, software executed by processing circuitry, or anycombination thereof. For illustrative purposes, the followingdescription may refer to elements mentioned above in connection withFIGS. 1-3 . It should be appreciated that the testing process 1000 mayinclude any number of additional or alternative tasks, the tasks neednot be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the testing process 1000 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 10 couldbe omitted from a practical embodiment of the testing process 1000 aslong as the intended overall functionality remains intact.

Similar to the fabrication model development process 500, theillustrated testing process 1000 receives or otherwise obtainsfabrication process measurements from different regions of differentsubstrates having different instances of a sensing element fabricatedthereon and receives or otherwise obtains measurement signal outputsfrom the different instances of sensing elements fabricated on thesubstrates (tasks 1002, 1004). As described above, a process measurementsystem 310 may analyze PCM regions 306 on a substrate 302 to obtain oneor more measurements of the physical characteristics of the respectivePCM region 306. The measurements of the physical characteristics of thePCM regions 306 are provided to the modeling system 330 which maintainsassociations between the respective measurements and the respectivelocations of the respective PCM regions 306 on the substrate 302. Atesting system 320 then tests or otherwise analyzes the differentsensing elements 304 fabricated on the substrate 302 using one or moreknown reference inputs to obtain, for each sensing element 304,reference measurement outputs generated or otherwise provided by therespective sensing element 304 in response to the reference input(s).For example, each sensing element 304 may be exposed to a knownreference glucose concentration to obtain a corresponding outputelectrical current measurement, EIS value, or the like. Fabricationprocess measurements are assigned to each of the sensing elements, andthe associations between the assigned fabrication process measurementsand reference measurement outputs for each sensing element aremaintained (tasks 1006, 1008), in a similar manner as described above(e.g., tasks 506, 508).

Still referring to FIG. 10 , the testing process 1000 generates orotherwise determines a predictive model for a characteristic of theoutput measurement signal from an instance of the sensing element as afunction of the fabrication process measurements based on therelationship between the reference measurement outputs and fabricationprocess measurements for different sensing elements (task 1010), in asimilar manner as described above (e.g., task 510). In this regard, themodeling system 330 may utilize machine learning or artificialintelligence techniques to determine which combination of fabricationprocess measurement parameters are correlated to or predictive of theoutput electrical current measurement in response to a known referenceglucose concentration, and then determine a corresponding equation,function, or model for calculating the magnitude, frequency, or othercharacteristic of the output electrical current generated by the sensingelement based on that set of input fabrication process measurementvariables. Thus, the model is capable of characterizing or mapping aparticular combination of one or more fabrication process measurementparameters to the output measurement signal.

For example, for a number of different instances of an interstitialsensing element, each instance of the interstitial sensing element maybe exposed to one or more reference glucose concentrations to obtaincorresponding reference output measurement(s) (e.g., reference valuesfor the output electrical current signal) associated with the referenceglucose concentration(s). Additionally, representative fabricationprocess measurement values for a number of different fabrication processmeasurement variables (e.g., GOx thickness, GOx activity, GLM thickness,WE platinum imaginary impedance, CE platinum imaginary impedance, HSAconcentration, etc.) may be obtained from or otherwise assigned to eachinstance of the interstitial sensing element as described above. Machinelearning, artificial intelligence, or other regression techniques maythen be utilized to determine an equation for calculating a predicted orexpected value for the output measurement as a function of a particularcombination of the fabrication process measurement variables based onthe relationships between the reference output measurement(s) and thedifferent fabrication process measurement variable values associatedwith the different instances of the interstitial sensing element.

Still referring to FIG. 10 , after developing a predictive modelcalculating a measurement output generated by a sensing element as afunction of input fabrication process measurement variables, the testingprocess 1000 continues by calculating or otherwise generating asimulated distribution of output measurements across the range of theinput fabrication process measurement variables (task 1012). In thisregard, the testing process 1000 calculates or otherwise determines anestimated output measurement for various combinations of values for thefabrication process measurement variables input to the predictive model.Thus, by independently varying the values for the fabrication processmeasurement variables input to the predictive model within theirspecified ranges (e.g., as dictated by the fabrication processes orother specifications), the predictive model can be utilized toextrapolate or interpolate the signal features of the sensing elementwithin the range of fabrication possibilities.

For example, given a predictive model for calculating the outputelectrical current measurement as a function of the working electrodeplatinum imaginary impedance, GOx activity, GOx thickness, HSAconcentration, and the GLM thickness, the testing process 1000calculates a corresponding estimated output electrical current value fordifferent combinations of working electrode platinum imaginaryimpedance, GOx activity, GOx thickness, HSA concentration, and the GLMthickness values from within the respective potential ranges for theworking electrode platinum imaginary impedance, GOx activity, GOxthickness, HSA concentration, and the GLM thickness. In this regard, afirst estimated output electrical current distribution (alternativelyreferred to herein as the low side simulated distribution) may becalculated for the combination of the high magnitude working electrodeplatinum imaginary impedance distribution, low potential GOx activitydistribution, low potential GOx thickness distribution, low potentialHSA concentration distribution, and the high potential GLM thicknessdistribution according to the fabrication processes, another estimatedoutput electrical current distribution (alternatively referred to hereinas the high side simulated distribution) may be calculated for thecombination of the low magnitude working electrode platinum imaginaryimpedance, high potential GOx activity distribution, high potential GOxthickness distribution, high potential HSA concentration distribution,and the low potential GLM thickness distribution. The respective inputvariables may be individually and independently varied (e.g., usingMonte Carlo techniques) around the respective end of the design rangefor the respective input variable to obtain a desired number of inputcombinations (e.g., 10,000 combinations) that are then input orotherwise provided to the predictive model to obtain a correspondingnumber of simulated outputs (e.g., 10,000 output samples) at therespective end of the expected output range. In this manner, thepredictive model is utilized to obtain simulated or estimated outputelectrical current values across the full range of potential valueswithin the two-dimensional variable space defined by the workingelectrode imaginary impedance, GOx activity, GOx thickness, HSAconcentration, and the GLM thickness input variables. The estimatedoutput electrical current values represent the expected distribution forthe output electrical current measurement across the input variablespace for the predictive model, which corresponds to the subset offabrication process measurements (or biological, chemical, electrical,and/or physical characteristics) that are predictive of or correlativeto the output electrical current measurement.

In exemplary embodiments, the testing process 1000 identifies orotherwise determines boundary or corner performance threshold values forthe normal operating region for the measurement output generated by thesensing element based on the simulated distribution for measurementoutput derived using the predictive model (task 1014). In this regard,the boundary or threshold values represent the corner cases (or processcorners) that delineate or otherwise define the normal operating rangefor the measurement output in response to a known reference input. Thecorner performance threshold or boundary values may be identified orotherwise determined based on a statistical analysis of the simulateddistribution of the measurement output. In this regard, it should benoted that there are any number of different statistical techniques thatmay be utilized to characterize a distribution of values to define anormal operating region within the distribution, and the subject matterdescribed herein is not limited to any particular implementation. Inexemplary embodiments, the testing system 320 stores or otherwisemaintains the corner threshold values defining the normal operatingregions in association with the reference input value for subsequentlytesting the output of sensing elements in response to that referenceinput.

For example, a statistical mean output electrical current value may becalculated or otherwise determined based on the simulated distributionof output electrical current values, with the corner threshold outputelectrical current values being determined based on the standarddeviation, variance, or other statistical measure of the simulateddistribution of the output electrical current values relative to themean output electrical current value. For example, the upper thresholdor boundary value to be associated with a given reference input stimulusmay be determined by adding three times the standard deviation of thehigh side simulated distribution to the mean output electrical currentvalue responsive to that reference input for the high side simulateddistribution, and the lower threshold or boundary value may bedetermined by subtracting three times the standard deviation of the lowside simulated distribution from the mean output electrical currentvalue of the low side simulated distribution. Thus, when a subsequentinstance of the sensing element generates an output electrical currentvalue in response to that reference input that is not within threestandard deviations of the mean of either the high or low side simulateddistributions, the instance of the sensing element may be discarded eventhough all other measurement parameters or characteristics of theinstance of the sensing element are within the desired ranges. In otherembodiments, the threshold or boundary values utilized to accept orreject may be different from the corner values derived from thesimulated distributions, for example, by adding or subtracting someoffset from the corner values. For example, the upper retentionthreshold may be determined by adding one and a half times the standarddeviation of the high side simulated distribution to the upper cornervalue, which is equal to the mean output electrical current value of thehigh side simulated distribution plus three standard deviations of thehigh side simulated distribution, such that any a subsequent instance ofthe sensing element that generates an output electrical current value inresponse to the reference input that is more than four and a halfstandard deviations greater than the mean of the of the high sidesimulated distribution is discarded, while output electrical currentvalues less than that retention threshold are retained. Thus, in suchembodiments, a subsequent instance of the sensing element could generatean output electrical current value that is outside the corner boundariesfrom the simulated distribution but still be retained provided theoutput electrical current value is close enough to the corner boundaryvalue (e.g., within one and a half standard deviations) and all othermeasurement parameters or characteristics of the instance of the sensingelement are within the desired ranges.

In exemplary embodiments, the testing process 1000 utilizes themodel-derived normal operating range performance thresholds to verify orotherwise validate the performance sensing elements after fabricationand filter or otherwise exclude non-conforming sensing elements prior tocalibration and subsequent deployment (task 1016). In this regard, thetesting process 1000 determines whether to accept or discard sensingelements when one or more of their output measurements in response to aknown reference input is outside the respective normal operating rangefor that output measurement. When an instance of the sensing elementgenerates an output measurement in response to a known stimulus that isgreater than or less than a respective corner threshold value definingan upper or lower boundary of the normal operating region, the instanceof the sensing element may be discarded or otherwise rejected (therebyreducing yield) without being calibrated or otherwise initialized inaccordance with the sensor initialization process 600 of FIG. 6 . Inthis regard, sensing elements or substrates may be rejected even thoughthe fabrication process measurements are within an acceptable range.Conversely, when the output measurement is within the corner performancethresholds defining the normal operating range, the sensorinitialization process 600 of FIG. 6 is performed to determinecalibration factors for the sensing element.

For example, continuing the above example, if the output electricalcurrent measurement generated by a particular sensing element inresponse to a reference glucose concentration is greater than or lessthan a corner threshold derived from the simulated distribution ofoutput electrical current measurements across the range of potentialworking electrode imaginary impedance, GOx activity, GOx thickness, HSAconcentration, and GLM thickness values, the sensing element may bediscarded or otherwise rejected by the testing system 320, even thoughthe working electrode imaginary impedance, GOx activity, GOx thickness,HSA concentration, and GLM thickness measurements for that sensingelement are within acceptable ranges. Conversely, if the outputelectrical current measurement generated by the sensing element inresponse to the reference glucose concentration is within cornerperformance thresholds derived from the simulated distribution of outputelectrical current measurements across the range of potential workingelectrode imaginary impedance, GOx activity, GOx thickness, HSAconcentration, and GLM thickness values, the sensing element proceeds tocalibration and deployment in accordance with the sensor initializationprocess 600 of FIG. 6 . In this regard, the working electrode imaginaryimpedance, GOx activity, GOx thickness, HSA concentration, and GLMthickness measurements for the sensing element may influence thecalibration factors associated with the sensing element, as describedabove.

By virtue of controlling for manufacturing variabilities using thetesting process 1000, the impact of process corners or processvariations on the sensing elements may be mitigated by ensuring thesensing elements that proceed to calibration and deployment functionwithin the normal or expected operating range for the fabricationprocess measurement constraints. In this manner, the performance ofsensing elements may be verified or otherwise validated in addition toverifying or validating the physical, biological, chemical, andelectrical characteristics prior to performing manufacturing calibrationand subsequent deployment. By filtering or otherwise removing processcorners or other potential performance outliers, the accuracy andreliability of the sensor initialization process 600 of FIG. 6 isimproved. Additionally, in some embodiments, the testing process 1000may be utilized to filter or otherwise remove sensing elements from thedata sets that are utilized by the fabrication model development process500 and/or the performance model development process 700, therebyimproving the accuracy and reliability of the resultant models utilizedby the sensor initialization process 600 and/or the measurement process800.

For the sake of brevity, conventional techniques related to glucosesensing and/or monitoring, sampling, filtering, calibration, closed-loopglucose control, and other functional aspects of the subject matter maynot be described in detail herein. In addition, certain terminology mayalso be used in the herein for the purpose of reference only, and thusis not intended to be limiting. For example, terms such as “first”,“second”, and other such numerical terms referring to structures do notimply a sequence or order unless clearly indicated by the context. Theforegoing description may also refer to elements or nodes or featuresbeing “connected” or “coupled” together. As used herein, unlessexpressly stated otherwise, “coupled” means that oneelement/node/feature is directly or indirectly joined to (or directly orindirectly communicates with) another element/node/feature, and notnecessarily mechanically.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or embodiments described herein are not intended tolimit the scope, applicability, or configuration of the claimed subjectmatter in any way. For example, the subject matter described herein isnot necessarily limited to the infusion devices and related systemsdescribed herein. Moreover, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the described embodiment or embodiments. It should beunderstood that various changes can be made in the function andarrangement of elements without departing from the scope defined by theclaims, which includes known equivalents and foreseeable equivalents atthe time of filing this patent application. Accordingly, details of theexemplary embodiments or other limitations described above should not beread into the claims absent a clear intention to the contrary.

What is claimed is:
 1. A processor-implemented method, comprising:obtaining one or more electrical signals from a sensing element of asensing arrangement, wherein the one or more electrical signals areinfluenced by a physiological condition in a body of a patient;obtaining, from a data storage element of the sensing arrangement,calibration data associated with the sensing element; converting, usingthe calibration data, the one or more electrical signals into one ormore calibrated measurement parameters; obtaining a performance modelassociated with the sensing element; obtaining personal data associatedwith the patient; and determining, using the performance model and basedon the personal data and the one or more calibrated measurementparameters, a calibrated output value indicative of the physiologicalcondition.
 2. The processor-implemented method of claim 1, wherein thecalibration data associated with the sensing element includes one ormore calibration factors or scaling factors for converting the one ormore electrical signals into the one or more calibrated measurementparameters.
 3. The processor-implemented method of claim 1, wherein thecalibration data associated with the sensing element is determined basedon estimated fabrication process measurement data for the sensingelement, and a calibration model that predicts output measurementparameters of the sensing element for a certain physiological conditionas a function of fabrication process measurement parameters of thesensing element.
 4. The processor-implemented method of claim 1, furthercomprising: obtaining fabrication process measurement data for asubstrate having the sensing element fabricated thereon; obtaining acalibration model associated with the sensing element; and determining,based on the fabrication process measurement data and using thecalibration model, the calibration data associated with the sensingelement for converting the one or more electrical signals into the oneor more calibrated measurement parameters, wherein the calibration datais stored in the data storage element of the sensing arrangement.
 5. Theprocessor-implemented method of claim 4, wherein obtaining thecalibration model comprises: obtaining fabrication process measurementparameters for a plurality of instances of the sensing element;obtaining output measurement parameters of the plurality of instances ofthe sensing element under a reference physiological condition; anddetermining the calibration model based on the fabrication processmeasurement parameters and the output measurement parameters of theplurality of instances of the sensing element under the referencephysiological condition.
 6. The processor-implemented method of claim 4,wherein: obtaining the fabrication process measurement data comprises:obtaining a plurality of fabrication process measurements from aplurality of process control monitor (PCM) regions on the substrate; anddetermining representative fabrication process measurement dataassociated with the sensing element based on a location of the sensingelement on the substrate relative to the plurality of PCM regions; anddetermining the calibration data associated with the sensing elementcomprises calculating the calibration data based on the representativefabrication process measurement data by inputting the representativefabrication process measurement data to the calibration model.
 7. Theprocessor-implemented method of claim 1, wherein the performance modelmaps the one or more calibrated measurement parameters into thecalibrated output value indicative of the physiological condition basedon the personal data.
 8. The processor-implemented method of claim 1,wherein the personal data includes at least one of an age, gender, bodymass index, height, weight, or demographic information associated withthe patient.
 9. The processor-implemented method of claim 1, wherein:the sensing element comprises an interstitial glucose sensing element;and determining the calibrated output value comprises determining, usingthe performance model, a calibrated sensor glucose measurement valuebased on the personal data and the one or more calibrated measurementparameters.
 10. The processor-implemented method of claim 9, wherein theone or more calibrated measurement parameters comprise: a calibratedmeasurement value of an output electrical current of the interstitialglucose sensing element; a calibrated measurement value of anelectrochemical impedance spectroscopy (EIS) of the interstitial glucosesensing element; a calibrated value characterizing an impedance of theinterstitial glucose sensing element based on the one or more electricalsignals; or a combination thereof.
 11. The processor-implemented methodof claim 9, further comprising: obtaining a plurality of historicalreference blood glucose measurements for a plurality of patients;obtaining a plurality of historical calibrated measurement parametervalues for the plurality of patients corresponding to the plurality ofhistorical reference blood glucose measurements; obtaining patient dataassociated with the plurality of patients; and determining theperformance model associated with the sensing element based onrelationships between the patient data, the plurality of historicalcalibrated measurement parameter values, and the plurality of historicalreference blood glucose measurements.
 12. A system comprising: one ormore processors; and one or more processor-readable media storinginstructions which, when executed by the one or more processors, causeperformance of: obtaining one or more electrical signals from a sensingelement of a sensing arrangement, wherein the one or more electricalsignals are influenced by a physiological condition in a body of apatient; obtaining, from a data storage element of the sensingarrangement, calibration data associated with the sensing element;converting, using the calibration data, the one or more electricalsignals into one or more calibrated measurement parameters; obtaining aperformance model associated with the sensing element; obtainingpersonal data associated with the patient; and determining, using theperformance model and based on the personal data and the one or morecalibrated measurement parameters, a calibrated output value indicativeof the physiological condition.
 13. The system of claim 12, wherein thecalibration data associated with the sensing element includes one ormore calibration factors or scaling factors for converting the one ormore electrical signals into the one or more calibrated measurementparameters.
 14. The system of claim 12, wherein the calibration dataassociated with the sensing element is determined based on estimatedfabrication process measurement data for the sensing element, and acalibration model that predicts output measurement parameters of thesensing element for a certain physiological condition as a function offabrication process measurement parameters of the sensing element. 15.The system of claim 12, wherein the instructions, when executed by theone or more processors, further cause performance of: obtainingfabrication process measurement data for a substrate having the sensingelement fabricated thereon; obtaining a calibration model associatedwith the sensing element; and determining, based on the fabricationprocess measurement data and using the calibration model, thecalibration data associated with the sensing element for converting theone or more electrical signals into the one or more calibratedmeasurement parameters, wherein the calibration data is stored in thedata storage element of the sensing arrangement.
 16. The system of claim12, wherein the performance model maps the one or more calibratedmeasurement parameters into the calibrated output value indicative ofthe physiological condition based on the personal data.
 17. The systemof claim 12, wherein: the sensing element comprises an interstitialglucose sensing element; and determining the calibrated output valuecomprises determining, using the performance model, a calibrated sensorglucose measurement value based on the personal data and the one or morecalibrated measurement parameters.
 18. One or more non-transitoryprocessor-readable media storing instructions which, when executed byone or more processors, cause performance of: obtaining one or moreelectrical signals from a sensing element of a sensing arrangement,wherein the one or more electrical signals are influenced by aphysiological condition in a body of a patient; obtaining, from a datastorage element of the sensing arrangement, calibration data associatedwith the sensing element; converting, using the calibration data, theone or more electrical signals into one or more calibrated measurementparameters; obtaining a performance model associated with the sensingelement; obtaining personal data associated with the patient; anddetermining, using the performance model and based on the personal dataand the one or more calibrated measurement parameters, a calibratedoutput value indicative of the physiological condition.
 19. The one ormore non-transitory processor-readable media of claim 18, wherein thecalibration data associated with the sensing element includes one ormore calibration factors or scaling factors for converting the one ormore electrical signals into the one or more calibrated measurementparameters.
 20. The one or more non-transitory processor-readable mediaof claim 18, wherein the performance model maps the one or morecalibrated measurement parameters into the calibrated output valueindicative of the physiological condition based on the personal data.